cs b551: elements of artificial intelligence

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CS B551: ELEMENTS OF ARTIFICIAL INTELLIGENCEInstructor: Kris Hauserhttp://cs.indiana.edu/~hauserk

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TOPICS Constraint Satisfaction Problems (CSPs)

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CONSTRAINT PROPAGATION

Place a queen in a square Remove the attacked squares from future

consideration

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Count the number of non-attacked squares in every row and column

Place a queen in a row or column with minimum number

Remove the attacked squares from future consideration

6655

556

5 5 5 5 5 6 7

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Repeat

34

4

335

4 3 3 3 4 5

6


4

3

234

3 3 3 4 3

7


4

2

213

3 3 3 1

8


2

2

1

2 2 1

8

9


2

1

1 2

10


1

1

11


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WHAT DO WE NEED? More than just a successor function and a

goal test

We also need: A means to propagate the constraints imposed

by one queen’s position on the positions of the other queens

An early failure test

Explicit representation of constraints Constraint propagation algorithms

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CONSTRAINT SATISFACTION PROBLEM (CSP) Set of variables {X1, X2, …, Xn} Each variable Xi has a domain Di of possible

values. Usually, Di is finite Set of constraints {C1, C2, …, Cp}

Each constraint relates a subset of variables by specifying the valid combinations of their values

Goal: Assign a value to every variable such that all constraints are satisfied

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MAP COLORING

7 variables {WA,NT,SA,Q,NSW,V,T} Each variable has the same domain:

{red, green, blue} No two adjacent variables have the same value:

WANT, WASA, NTSA, NTQ, SAQ, SANSW, SAV, QNSW, NSWV

WANT

SA

Q

NSWV

T

WANT

SA

Q

NSWV

T

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8-QUEEN PROBLEM 8 variables Xi, i = 1 to 8 The domain of each variable is: {1,2,…,8} Constraints are of the forms:

Xi = k Xj k for all j = 1 to 8, ji Similar constraints for diagonals

All constraints are binary (in this class)

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SUDOKU 81 variables Domain of each variable:

{1,…,9} Constraints:

xixj for all ij in same row, same col, same cell

xi=vi for fixed cells

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STREET PUZZLE1 2 3 4 5

Ni = {English, Spaniard, Japanese, Italian, Norwegian}Ci = {Red, Green, White, Yellow, Blue}Di = {Tea, Coffee, Milk, Fruit-juice, Water}Ji = {Painter, Sculptor, Diplomat, Violinist, Doctor}Ai = {Dog, Snails, Fox, Horse, Zebra}The Englishman lives in the Red houseThe Spaniard has a DogThe Japanese is a PainterThe Italian drinks TeaThe Norwegian lives in the first house on the leftThe owner of the Green house drinks CoffeeThe Green house is on the right of the White houseThe Sculptor breeds SnailsThe Diplomat lives in the Yellow houseThe owner of the middle house drinks MilkThe Norwegian lives next door to the Blue houseThe Violinist drinks Fruit juiceThe Fox is in the house next to the Doctor’sThe Horse is next to the Diplomat’s

Who owns the Zebra?Who drinks Water?

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i,j[1,5], ij, Ni Nj

i,j[1,5], ij, Ci Cj

...

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(Ni = English) (Ci = Red)

(Ni = Japanese) (Ji = Painter)(N1 = Norwegian)

left as an exercise

(Ci = White) (Ci+1 = Green)(C5 White)(C1 Green)

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(Ni = English) (Ci = Red)

(Ni = Japanese) (Ji = Painter)(N1 = Norwegian)

(Ci = White) (Ci+1 = Green)(C5 White)(C1 Green)

unary constraints

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Ni = {English, Spaniard, Japanese, Italian, Norwegian}Ci = {Red, Green, White, Yellow, Blue}Di = {Tea, Coffee, Milk, Fruit-juice, Water}Ji = {Painter, Sculptor, Diplomat, Violinist, Doctor}Ai = {Dog, Snails, Fox, Horse, Zebra}The Englishman lives in the Red houseThe Spaniard has a DogThe Japanese is a PainterThe Italian drinks TeaThe Norwegian lives in the first house on the left N1 = NorwegianThe owner of the Green house drinks CoffeeThe Green house is on the right of the White houseThe Sculptor breeds SnailsThe Diplomat lives in the Yellow houseThe owner of the middle house drinks Milk D3 = MilkThe Norwegian lives next door to the Blue houseThe Violinist drinks Fruit juiceThe Fox is in the house next to the Doctor’sThe Horse is next to the Diplomat’s 21

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Ni = {English, Spaniard, Japanese, Italian, Norwegian}Ci = {Red, Green, White, Yellow, Blue}Di = {Tea, Coffee, Milk, Fruit-juice, Water}Ji = {Painter, Sculptor, Diplomat, Violinist, Doctor}Ai = {Dog, Snails, Fox, Horse, Zebra}The Englishman lives in the Red house C1 RedThe Spaniard has a Dog A1 DogThe Japanese is a PainterThe Italian drinks TeaThe Norwegian lives in the first house on the left N1 = NorwegianThe owner of the Green house drinks CoffeeThe Green house is on the right of the White houseThe Sculptor breeds SnailsThe Diplomat lives in the Yellow houseThe owner of the middle house drinks Milk D3 = MilkThe Norwegian lives next door to the Blue houseThe Violinist drinks Fruit juice J3 ViolinistThe Fox is in the house next to the Doctor’sThe Horse is next to the Diplomat’s

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TASK SCHEDULING

Four tasks T1, T2, T3, and T4 are related by time constraints: T1 must be done during T3 T2 must be achieved before T1 starts T2 must overlap with T3 T4 must start after T1 is complete

Are the constraints compatible? What are the possible time relations between two tasks? What if the tasks use resources in limited supply?

How to formulate this problem as a CSP?

T1

T2

T3

T4

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3-SAT n Boolean variables u1, ..., un

p constraints of the form ui* uj* uk*= 1

where u* stands for either u or u

Known to be NP-complete

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FINITE VS. INFINITE CSP Finite CSP: each variable has a finite domain

of values Infinite CSP: some or all variables have an

infinite domainE.g., linear programming problems over the reals:

We will only consider finite CSP

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CSP AS A SEARCH PROBLEM n variables X1, ..., Xn

Valid assignment: {Xi1 vi1, ..., Xik vik}, 0 k n, such that the values vi1, ..., vik satisfy all constraints relating the variables Xi1, ..., Xik

Complete assignment: one where k = n [if all variable domains have size d, there are O(dn) complete assignments]

States: valid assignments Initial state: empty assignment {}, i.e. k = 0 Successor of a state: {Xi1vi1, ..., Xikvik} {Xi1vi1, ..., Xikvik, Xik+1vik+1} Goal test: k = n

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{Xi1vi1, ..., Xikvik}

r = n-k variables with s values rs branching factor

{Xi1vi1, ..., Xikvik, Xik+1vik+1}

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A KEY PROPERTY OF CSP: COMMUTATIVITY The order in which variables are assigned

values has no impact on the reachable complete valid assignments

Hence:1) One can expand a node N by first selecting

one variable X not in the assignment A associated with N and then assigning every value v in the domain of X [ big reduction in branching factor]

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29The depth of the solutions in the search tree is un-changed (n)

{Xi1vi1, ..., Xikvik}

{Xi1vi1, ..., Xikvik, Xik+1vik+1}

r = n-k variables with s values rs branching factorr = n-k variables with s values s branching factor

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4 variables X1, ..., X4 Let the valid assignment of N be:

A = {X1 v1, X3 v3} For example pick variable X4 Let the domain of X4 be {v4,1, v4,2, v4,3} The successors of A are all the valid

assignments among: {X1 v1, X3 v3 , X4 v4,1 }

{X1 v1, X3 v3 , X4 v4,2 } {X1 v1, X3 v3 , X4 v4,2 }

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A KEY PROPERTY OF CSP: COMMUTATIVITY The order in which variables are assigned

values has no impact on the reachable complete valid assignments

Hence:1) One can expand a node N by first selecting one

variable X not in the assignment A associated with N and then assigning every value v in the domain of X [ big reduction in branching factor]

2) One need not store the path to a node Backtracking search algorithm

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BACKTRACKING SEARCH Essentially a simplified depth-first algorithm

using recursion

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BACKTRACKING SEARCH(3 VARIABLES)

Assignment = {}33

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Assignment = {(X1,v11)}

X1

v11

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Assignment = {(X1,v11), (X3,v31)}

X1

v11

v31

X3

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X1

v11

v31

X3

X2 Assume that no value of X2leads to a valid assignment

Then, the search algorithm backtracks to the previous variable (X3) and tries another value

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X1

v11

X3

v32v31

X2

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X1

v11

X3

v32

X2

Assume again that no value of X2 leads to a valid assignment

The search algorithm backtracks to the previous variable (X3) and tries another value. But assume that X3 has only two possible values. The algorithm backtracks to X1

v31

X2

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Assignment = {(X1,v12)}

X1

v11

X3

v32

X2

v31

X2

v12

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X1

v11

X3

v32

X2

v31

X2

v12

v21

X2

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X1

v11

X3

v32

X2

v31

X2

v12

v21

X2

The algorithm need not considerthe variables in the same order inthis sub-tree as in the other

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Assignment = {(X1,v12), (X2,v21), (X3,v32)}

X1

v11

X3

v32

X2

v31

X2

v12

v21

X2

v32

X3

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X1

v11

X3

v32

X2

v31

X2

v12

v21

X2

v32

X3The algorithm need not consider the valuesof X3 in the same order in this sub-tree

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X1

v11

X3

v32

X2

v31

X2

v12

v21

X2

v32

X3Since there are onlythree variables, theassignment is complete

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BACKTRACKING ALGORITHMCSP-BACKTRACKING(A)

1. If assignment A is complete then return A2. X select a variable not in A3. D select an ordering on the domain of X4. For each value v in D do

a. Add (Xv) to Ab. If A is valid then

i. result CSP-BACKTRACKING(A)ii. If result failure then return result

c. Remove (Xv) from A5. Return failure

Call CSP-BACKTRACKING({})[This recursive algorithm keeps too much data in memory. An iterative version could save memory (left as an exercise)] 45

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CRITICAL QUESTIONS FOR THE EFFICIENCY OF CSP-BACKTRACKING

CSP-BACKTRACKING(A)1. If assignment A is complete then return A2. X select a variable not in A3. D select an ordering on the domain of X4. For each value v in D do

a. Add (Xv) to Ab. If a is valid then

i. result CSP-BACKTRACKING(A)ii. If result failure then return result

c. Remove (Xv) from A5. Return failure

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CRITICAL QUESTIONS FOR THE EFFICIENCY OF CSP-BACKTRACKING 1. Which variable X should be assigned a

value next?

2. In which order should X’s values be assigned?

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CRITICAL QUESTIONS FOR THE EFFICIENCY OF CSP-BACKTRACKING 1. Which variable X should be assigned a

value next?The current assignment may not lead to any solution, but the algorithm does not know it yet. Selecting the right variable X may help discover the contradiction more quickly

2. In which order should X’s values be assigned?

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CRITICAL QUESTIONS FOR THE EFFICIENCY OF CSP-BACKTRACKING 1. Which variable X should be assigned a value

next?The current assignment may not lead to any solution, but the algorithm does not know it yet. Selecting the right variable X may help discover the contradiction more quickly

2. In which order should X’s values be assigned?The current assignment may be part of a solution. Selecting the right value to assign to X may help discover this solution more quickly

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CRITICAL QUESTIONS FOR THE EFFICIENCY OF CSP-BACKTRACKING 1. Which variable X should be assigned a value

next?The current assignment may not lead to any solution, but the algorithm does not know it yet. Selecting the right variable X may help discover the contradiction more quickly

2. In which order should X’s values be assigned?The current assignment may be part of a solution. Selecting the right value to assign to X may help discover this solution more quickly

More on these questions very soon ...

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FORWARD CHECKING

A simple constraint-propagation technique:

Assigning the value 5 to X1 leads to removing values from the domains of X2, X3, ..., X8

12345678

X1 X2 X3 X4 X5 X6 X7 X8

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FORWARD CHECKING IN MAP COLORING

WA NT Q NSW V SA TRGB RGB RGB RGB RGB RGB RGB

TWA

NT

SA

Q

NSWV

Constraint graph

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WA NT Q NSW V SA TRGB RGB RGB RGB RGB RGB RGBR RGB RGB RGB RGB RGB RGB

TWA

NT

SA

Q

NSWV

Forward checking removes the value Red of NT and of SA

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WA NT Q NSW V SA TRGB RGB RGB RGB RGB RGB RGBR GB RGB RGB RGB GB RGBR GB G RGB RGB GB RGB

TWA

NT

SA

Q

NSWV

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WA NT Q NSW V SA TRGB RGB RGB RGB RGB RGB RGBR GB RGB RGB RGB GB RGBR B G RB RGB B RGBR B G RB B B RGB

TWA

NT

SA

Q

NSWV

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WA NT Q NSW V SA TRGB RGB RGB RGB RGB RGB RGBR GB RGB RGB RGB GB RGBR B G RB RGB B RGBR B G RB B B RGB

Empty set: the current assignment {(WA R), (Q G), (V B)}does not lead to a solution

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FORWARD CHECKING (GENERAL FORM)Whenever a pair (Xv) is added to assignment A do: For each variable Y not in A do:

For every constraint C relating Y to the variables in A do:

Remove all values from Y’s domain that do not satisfy C

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MODIFIED BACKTRACKING ALGORITHM

CSP-BACKTRACKING(A, var-domains)1. If assignment A is complete then return A2. X select a variable not in A3. D select an ordering on the domain of X4. For each value v in D do

a. Add (Xv) to Ab. var-domains forward checking(var-domains, X, v,

A)c. If no variable has an empty domain then

(i) result CSP-BACKTRACKING(A, var-domains)(ii) If result failure then return result

d. Remove (Xv) from A5. Return failure

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No need any more to verify that A is valid







60Need to pass down the updated variable domains







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1) Which variable Xi should be assigned a value next? Most-constrained-variable heuristic Most-constraining-variable heuristic

2) In which order should its values be assigned? Least-constraining-value heuristic

Keep in mind that all variables must eventually get a value, while only one value from a domain must be assigned to each variable

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MOST-CONSTRAINED-VARIABLE HEURISTIC

1) Which variable Xi should be assigned a value next?Select the variable with the smallest remaining domain[Rationale: Minimize the branching factor]

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8-QUEENS

4 3 2 3 4 Numbersof values foreach un-assignedvariable

New assignment

Forward checking

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8-QUEENS

3 2 1 3 New numbersof values foreach un-assignedvariable

New assignment

Forward checking

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MAP COLORING

SA’s remaining domain has size 1 (value B remaining) Q’s remaining domain has size 2 NSW’s, V’s, and T’s remaining domains have size 3 Select SA

WA

NT

SA

Q

NSWV

T

WA

NT

SA

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MOST-CONSTRAINING-VARIABLE HEURISTIC 1) Which variable Xi should be assigned a

value next?Among the variables with the smallest remaining domains (ties with respect to the most-constrained-variable heuristic), select the one that appears in the largest number of constraints on variables not in the current assignment[Rationale: Increase future elimination of values, to reduce future branching factors]

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MAP COLORING

Before any value has been assigned, all variables have a domain of size 3, but SA is involved in more constraints (5) than any other variable

Select SA and assign a value to it (e.g., Blue)

WA

NT

SA

Q

NSWV

T

SA

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LEAST-CONSTRAINING-VALUE HEURISTIC

2) In which order should X’s values be assigned?Select the value of X that removes the smallest number of values from the domains of those variables which are not in the current assignment

[Rationale: Since only one value will eventually be assigned to X, pick the least-constraining value first, since it is the most likely not to lead to an invalid assignment][Note: Using this heuristic requires performing a forward-checking step for every value, not just for the selected value]

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MAP COLORING

Q’s domain has two remaining values: Blue and Red

Assigning Blue to Q would leave 0 value for SA, while assigning Red would leave 1 value

{}

WA

NT

SA

Q

NSWV

T

WA

NT

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MAP COLORING

Q’s domain has two remaining values: Blue and Red

Assigning Blue to Q would leave 0 value for SA, while assigning Red would leave 1 value

So, assign Red to Q

{Blue}

WA

NT

SA

Q

NSWV

T

WA

NT

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1) Most-constrained-variable heuristic2) Most-constraining-variable heuristic

3) Least-constraining-value heuristic






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APPLICATIONS OF CSP CSP techniques are widely used Applications include:

Crew assignments to flights Management of transportation fleet Flight/rail schedules Job shop scheduling Task scheduling in port operations Design, including spatial layout design

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RECAP CSPs Backtracking search Constraint propagation

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HOMEWORK R&N 6.3-5

cs b551: elements of artificial intelligence

Documents

constraint propagation101

constraint propagation92

constraint propagation82

constraint propagation74

constraint propagation64

red housethe spaniard

finiteset of constraints

jisimilar constraints